Image data captured by an image sensor or received from other data sources is often processed in an image processing pipeline before further processing or consumption. For example, raw image data may be corrected, filtered, or otherwise modified before being provided to subsequent components such as a video encoder. To perform corrections or enhancements for captured image data, various components, unit stages or modules may be employed.
Such an image processing pipeline may be structured so that corrections or enhancements to the captured image data can be performed in an expedient way without consuming other system resources. Although many image processing algorithms may be performed by executing software programs on a central processing unit (CPU), execution of such programs on the CPU would consume significant bandwidth of the CPU and other peripheral resources as well as increase power consumption. Hence, image processing pipelines are often implemented as a hardware component separate from the CPU and dedicated to perform one or more image processing algorithms.
One of processes of the image processing pipeline is noise reduction. Noise to an image data can be introduced during various operations such as image capturing, transmission, and transformation. The nature of the noise removal problem depends on the type of the noise corrupting the image data, and different types of linear and nonlinear filtering methods are often used to reduce noise in image data. Linear filters are not able to effectively eliminate impulse noise as they have a tendency to blur the edges of an image. On the other hand nonlinear filters are suited for dealing with impulse noise.